Recently, rapid technical advances in network have made it possible for anyone to easily produce, process, and distribute a variety of multimedia contents over Internet, etc. Also, with rapid advances in media acquisition and processing techniques, such things as media production with a small camcorder and other equipments, media creation using various application programs, media processing based on other media already produced, and media distribution through P2P (peer-to-peer), Internet portal site, diverse sharing programs, UCC (user creative contents), etc. became part of every day life for many people.
Therefore, in multimedia services, there is a need for a digital video management system capable of managing digital videos more efficiently, which involves such techniques as identifying source and information of encoded digital media that past techniques could not identify the source and information, searching for identical digital videos yet having different analog signals due to exposure to diverse noises, and tracing or monitoring videos being broadcasted through a variety of media or distributed over a giant network such as Internet.
However, one problem of the conventional digital video management system was that it could not perform simple storage, searching and retaining a video itself for several reasons such as diverse sizes and different standards of all digital videos stored, or noises.
One of techniques useful for digital video management services includes resolving an input video into still images to find out a video file. According to this technique, a video search manager can identify a key image semi-automatically, extract a collection of video files to be searched using the obtained key image, compare and analyze contents of the key image with contents of video files to be searched to find out a video file that matches the key image using an image comparison technology. However, since this technique is based on image search to find a matching video file, it still requires large amounts of data processing, which is not appropriate for efficient management of large capacity digital video data.
Another technique related to digital video management services is disclosed in an article by Z. Rasheed, Y. Sheikh, and M. Shah, entitled “On the Use of Computable Features for Film Classification”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 15, No. 1, pp. 52-64, 2005, wherein computable visual cues are presented for the classification of films into genres. Specifically, for the detection of scene changes, color space is first converted into HSV (hue-saturation-value) to build histograms each having 8, 4, and 4 bins. Then, histogram intersection of consecutive frames is obtained and a change of scenes is detected based on anisotropic diffusion algorithm. The detected change of scenes is used to classify films into genres. However, even this film classification technology does not still propose a more efficient management method for large capacity digital video data.
Therefore, a need has existed for a novel digital video management method to minimize the use of resources such as time, storage space, etc. required for the management of large capacity digital videos.